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https://github.com/aursiber/MareyMap

install.packages(MareyMap) install.packages(tcltk) install.packages(tkrplot) install.packages(tools)

## Warning: running command ''/usr/bin/otool' -L '/Library/Frameworks/R.framework/
## Resources/library/tcltk/libs//tcltk.so'' had status 1
## 
## Attaching package: 'MareyMap'
## The following objects are masked from 'package:stats':
## 
##     df, setNames
## Loading required package: viridisLite
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
##     mkr  set        map    phys   gen  vld slidingwindow.3 slidingwindow
## 1 41199 vcar LR999924.1  377201 1.111 TRUE            0.76          0.00
## 2 41202 vcar LR999924.1  406369 1.111 TRUE            0.76          0.00
## 3 41230 vcar LR999924.1  702834 1.111 TRUE            1.08          0.00
## 4 41246 vcar LR999924.1  942878 1.111 TRUE            1.44          1.89
## 5 41256 vcar LR999924.1  943232 1.111 TRUE            1.44          1.89
## 6 41276 vcar LR999924.1 1080480 1.111 TRUE            1.58          1.79
##   slidingwindow.2 slidingwindow.4 vld.1 loess
## 1            0.00            0.76  TRUE    NA
## 2            0.00            0.76  TRUE  1.11
## 3            0.00            1.08  TRUE  1.24
## 4            1.89            1.44  TRUE  1.34
## 5            1.89            1.44  TRUE  1.34
## 6            1.79            1.58  TRUE  1.49
## 'data.frame':    1696 obs. of  12 variables:
##  $ mkr            : int  41199 41202 41230 41246 41256 41276 41304 41335 41356 41363 ...
##  $ set            : Factor w/ 1 level "vcar": 1 1 1 1 1 1 1 1 1 1 ...
##  $ map            : chr  "LR999924.1" "LR999924.1" "LR999924.1" "LR999924.1" ...
##  $ phys           : int  377201 406369 702834 942878 943232 1080480 1356020 1413500 1535700 1759540 ...
##  $ gen            : num  1.11 1.11 1.11 1.11 1.11 ...
##  $ vld            : logi  TRUE TRUE TRUE TRUE TRUE TRUE ...
##  $ slidingwindow.3: num  0.76 0.76 1.08 1.44 1.44 1.58 1.55 1.74 1.61 1.27 ...
##  $ slidingwindow  : num  0 0 0 1.89 1.89 1.79 2.61 2.61 2.59 1.67 ...
##  $ slidingwindow.2: num  0 0 0 1.89 1.89 1.79 2.61 2.61 2.59 1.67 ...
##  $ slidingwindow.4: num  0.76 0.76 1.08 1.44 1.44 1.58 1.55 1.74 1.61 1.27 ...
##  $ vld.1          : logi  TRUE TRUE TRUE TRUE TRUE TRUE ...
##  $ loess          : num  NA 1.11 1.24 1.34 1.34 1.49 1.48 1.42 0.58 0.22 ...
## [1] "Total length of genome, incl W: 410758454"
## Joining by: map
##       mkr          set           map                 phys         
##  Min.   :   19   vcar:1696   Length:1696        Min.   :   14622  
##  1st Qu.: 9654               Class :character   1st Qu.: 3867315  
##  Median :20822               Mode  :character   Median : 7301015  
##  Mean   :20844                                  Mean   : 7337513  
##  3rd Qu.:31587                                  3rd Qu.:10630375  
##  Max.   :42517                                  Max.   :16942500  
##                                                                   
##       gen          vld          slidingwindow.3  slidingwindow    
##  Min.   : 0.00   Mode:logical   Min.   : 0.000   Min.   :  0.000  
##  1st Qu.:13.34   TRUE:1696      1st Qu.: 1.675   1st Qu.:  0.000  
##  Median :24.52   NA's:0         Median : 3.040   Median :  2.460  
##  Mean   :25.03                  Mean   : 3.500   Mean   :  4.305  
##  3rd Qu.:36.75                  3rd Qu.: 4.875   3rd Qu.:  5.330  
##  Max.   :61.21                  Max.   :15.520   Max.   :283.070  
##                                 NA's   :5        NA's   :43       
##  slidingwindow.2   slidingwindow.4    vld.1             loess        
##  Min.   :  0.000   Min.   : 0.000   Mode :logical   Min.   :-12.520  
##  1st Qu.:  0.000   1st Qu.: 1.675   FALSE:1         1st Qu.:  1.330  
##  Median :  2.460   Median : 3.040   TRUE :1695      Median :  2.845  
##  Mean   :  4.305   Mean   : 3.500   NA's :0         Mean   :  3.567  
##  3rd Qu.:  5.330   3rd Qu.: 4.875                   3rd Qu.:  4.890  
##  Max.   :283.070   Max.   :15.520                   Max.   : 61.320  
##  NA's   :43        NA's   :5                        NA's   :62       
##    chr_length         chr_start           genome_pos           rel_pos        
##  Min.   : 6166334   Min.   :        0   Min.   :   377201   Min.   : 0.09783  
##  1st Qu.:13236624   1st Qu.: 66099924   1st Qu.: 77390899   1st Qu.:28.70873  
##  Median :14868897   Median :175009806   Median :187510556   Median :51.82503  
##  Mean   :14332339   Mean   :178197377   Mean   :185534890   Mean   :51.00087  
##  3rd Qu.:16000791   3rd Qu.:275115552   3rd Qu.:282160487   3rd Qu.:73.57617  
##  Max.   :17040296   Max.   :404592120   Max.   :410175120   Max.   :99.90938  
## 
## Joining by: map
##      map                loess          loess_sd           1mb        
##  Length:31          Min.   :0.000   Min.   : 0.000   Min.   : 1.844  
##  Class :character   1st Qu.:2.907   1st Qu.: 2.041   1st Qu.: 2.774  
##  Mode  :character   Median :3.395   Median : 2.766   Median : 3.937  
##                     Mean   :3.549   Mean   : 3.532   Mean   : 5.699  
##                     3rd Qu.:4.149   3rd Qu.: 4.212   3rd Qu.: 4.652  
##                     Max.   :7.244   Max.   :10.598   Max.   :47.964  
##      1mb_sd            2mb            2mb_sd      no_overlap_1mb  
##  Min.   : 2.277   Min.   :1.842   Min.   :1.189   Min.   : 1.844  
##  1st Qu.: 2.793   1st Qu.:3.056   1st Qu.:1.793   1st Qu.: 2.774  
##  Median : 3.496   Median :3.623   Median :2.218   Median : 3.937  
##  Mean   : 7.362   Mean   :3.834   Mean   :2.420   Mean   : 5.699  
##  3rd Qu.: 4.531   3rd Qu.:4.282   3rd Qu.:2.681   3rd Qu.: 4.652  
##  Max.   :56.134   Max.   :7.127   Max.   :6.578   Max.   :47.964  
##  no_overlap_1mb_sd no_overlap_2mb  no_overlap_2mb_sd   map_length   
##  Min.   : 2.277    Min.   :1.842   Min.   :1.189     Min.   :20.06  
##  1st Qu.: 2.793    1st Qu.:3.056   1st Qu.:1.793     1st Qu.:41.70  
##  Median : 3.496    Median :3.623   Median :2.218     Median :46.69  
##  Mean   : 7.362    Mean   :3.834   Mean   :2.420     Mean   :44.36  
##  3rd Qu.: 4.531    3rd Qu.:4.282   3rd Qu.:2.681     3rd Qu.:49.58  
##  Max.   :56.134    Max.   :7.127   Max.   :6.578     Max.   :61.21  
##    chr_length       chr_start              rate          markers      
##  Min.   : 6.166   Min.   :        0   Min.   :1.750   Min.   : 10.00  
##  1st Qu.:11.515   1st Qu.:121554284   1st Qu.:2.870   1st Qu.: 39.50  
##  Median :13.916   Median :233874477   Median :3.362   Median : 58.00  
##  Mean   :13.250   Mean   :223364757   Mean   :3.422   Mean   : 54.71  
##  3rd Qu.:15.500   3rd Qu.:332593828   3rd Qu.:3.869   3rd Qu.: 68.50  
##  Max.   :17.040   Max.   :404592120   Max.   :5.341   Max.   :114.00  
##  marker_density 
##  Min.   :1.355  
##  1st Qu.:3.131  
##  Median :4.283  
##  Mean   :3.945  
##  3rd Qu.:4.679  
##  Max.   :6.690
## [1] "Markers in map: 1696"
## [1] "Mean recombination rate: 3.422"
## [1] "Recombination rate Z: 2.478"
## [1] "Recombination rate mean automomes: 3.454"
## [1] "Window based (2Mb):"
## [1] "Mean recombination rate (chr based): 3.834"
## [1] "sd recombination rate (chr based): 1.197"
## [1] "Mean recombination rate (overall windows): 3.5"
## [1] "sd (overall windows): 2.52"
## [1] "Recombination rate Z: 2.728"
## [1] "Recombination rate mean automomes: 3.87"
#test 
rate_lm <- lm(rec_rate_mean$rate~rec_rate_mean$chr_length + rec_rate_mean$marker_density)
summary(rate_lm)
## 
## Call:
## lm(formula = rec_rate_mean$rate ~ rec_rate_mean$chr_length + 
##     rec_rate_mean$marker_density)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.56312 -0.31054  0.01502  0.42921  1.32246 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   4.97531    0.56414   8.819 1.43e-09 ***
## rec_rate_mean$chr_length     -0.16540    0.06065  -2.727   0.0109 *  
## rec_rate_mean$marker_density  0.16190    0.15197   1.065   0.2958    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.67 on 28 degrees of freedom
## Multiple R-squared:  0.2481, Adjusted R-squared:  0.1944 
## F-statistic: 4.619 on 2 and 28 DF,  p-value: 0.01847
cor.test(rec_rate_mean$rate, rec_rate_mean$chr_length)
## 
##  Pearson's product-moment correlation
## 
## data:  rec_rate_mean$rate and rec_rate_mean$chr_length
## t = -2.84, df = 29, p-value = 0.008164
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.7043871 -0.1343412
## sample estimates:
##        cor 
## -0.4664759
cor.test(rec_rate_mean$rate, rec_rate_mean$marker_density)
## 
##  Pearson's product-moment correlation
## 
## data:  rec_rate_mean$rate and rec_rate_mean$marker_density
## t = -1.2139, df = 29, p-value = 0.2346
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5327314  0.1457991
## sample estimates:
##        cor 
## -0.2199017
rate_lm_window <- lm(rec_rate_mean$`2mb`~rec_rate_mean$chr_length + rec_rate_mean$marker_density)
summary(rate_lm_window)
## 
## Call:
## lm(formula = rec_rate_mean$`2mb` ~ rec_rate_mean$chr_length + 
##     rec_rate_mean$marker_density)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.07950 -0.48081  0.00692  0.32981  1.77535 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   7.800647   0.706866  11.036 1.05e-11 ***
## rec_rate_mean$chr_length     -0.298769   0.075994  -3.931 0.000505 ***
## rec_rate_mean$marker_density -0.002107   0.190423  -0.011 0.991248    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8395 on 28 degrees of freedom
## Multiple R-squared:  0.5411, Adjusted R-squared:  0.5083 
## F-statistic: 16.51 on 2 and 28 DF,  p-value: 1.835e-05
anova(rate_lm_window)
## Analysis of Variance Table
## 
## Response: rec_rate_mean$`2mb`
##                              Df  Sum Sq Mean Sq F value    Pr(>F)    
## rec_rate_mean$chr_length      1 23.2728 23.2728 33.0184 3.631e-06 ***
## rec_rate_mean$marker_density  1  0.0001  0.0001  0.0001    0.9912    
## Residuals                    28 19.7356  0.7048                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
par(mfrow=c(2,2))
plot(rate_lm_window)

cor.test(rec_rate_mean$`2mb`, rec_rate_mean$chr_length)
## 
##  Pearson's product-moment correlation
## 
## data:  rec_rate_mean$`2mb` and rec_rate_mean$chr_length
## t = -5.8479, df = 29, p-value = 2.42e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.8645890 -0.5156852
## sample estimates:
##       cor 
## -0.735609
cor.test(rec_rate_mean$`2mb`, rec_rate_mean$marker_density)
## 
##  Pearson's product-moment correlation
## 
## data:  rec_rate_mean$`2mb` and rec_rate_mean$marker_density
## t = -3.4234, df = 29, p-value = 0.001863
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.7485300 -0.2248958
## sample estimates:
##        cor 
## -0.5364838
cor.test(rec_rate_mean$loess, rec_rate_mean$chr_length)
## 
##  Pearson's product-moment correlation
## 
## data:  rec_rate_mean$loess and rec_rate_mean$chr_length
## t = 0.52591, df = 29, p-value = 0.6029
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.2663150  0.4365038
## sample estimates:
##        cor 
## 0.09719722
cor.test(rec_rate_mean$loess, rec_rate_mean$marker_density)
## 
##  Pearson's product-moment correlation
## 
## data:  rec_rate_mean$loess and rec_rate_mean$marker_density
## t = 0.83874, df = 29, p-value = 0.4085
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.2120056  0.4819540
## sample estimates:
##       cor 
## 0.1538954
#plot marey maps
ggplot(rec_rate, aes(rec_rate$phys, rec_rate$gen)) +
  geom_point() +
  facet_wrap(~rec_rate$map, scales="free") +
  ylab("cM") +
  xlab("Mb") +
  theme(panel.background = element_blank(),
        axis.line = element_line(size = 1),
        axis.text = element_text(size = 10),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.text = element_text(size = 10))

#plot recombination rate per Mb (loess)
ggplot(rec_rate, aes(rec_rate$phys, rec_rate$loess)) +
  geom_point() +
  geom_line() +
  facet_wrap(~rec_rate$map, scales = "free") +
  ylab("cM/Mb") +
  xlab("Mb") +
  theme(panel.background = element_blank(),
        axis.line = element_line(size = 1),
        axis.text = element_text(size = 10),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.text = element_text(size = 10))
## Warning: Removed 62 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_path).

#plot recombination rate per Mb (2 Mb window)
ggplot(rec_rate, aes(rec_rate$phys, rec_rate$slidingwindow.3)) +
  geom_point() +
  geom_line() +
  facet_wrap(~rec_rate$map, scales = "free") +
  ylab("cM/Mb") +
  xlab("Mb") +
  theme(panel.background = element_blank(),
        axis.line = element_line(size = 1),
        axis.text = element_text(size = 10),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.text = element_text(size = 10))
## Warning: Removed 5 rows containing missing values (geom_point).

#in one plot
ggplot(rec_rate, aes(rec_rate$phys, log(rec_rate$slidingwindow.3))) +
  geom_point() +
  geom_line(aes(colour=rec_rate$map)) +
  #facet_wrap(~rec_rate$map, scales = "free") +
  guides(colour=FALSE) +
  ylab("cM/Mb") +
  xlab("Mb") +
  theme(panel.background = element_blank(),
        axis.line = element_line(size = 1),
        axis.text = element_text(size = 10),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.text = element_text(size = 10))
## Warning: Removed 5 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_path).

#chr length and rate 
ggplot(rec_rate_mean, aes(rec_rate_mean$chr_length,rec_rate_mean$rate)) +
  geom_point() +
  geom_smooth(method = "lm") +
  labs(title = "Correlation rec rate (maplength/chr size) and chromosome size") +
  ylab("cM/Mb") +
  xlab("Chromosome length (Mbp)") +
  theme(panel.background = element_blank(),
        axis.line = element_line(size = 1),
        axis.text = element_text(size = 10),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.text = element_text(size = 10))

#chr length and rate 
ggplot(rec_rate_mean, aes(rec_rate_mean$chr_length,rec_rate_mean$loess)) +
  geom_point() +
  geom_smooth(method = "lm") +
  labs(title = "Correlation rec rate (loess) and chromosome size") +
  ylab("cM/Mb") +
  xlab("Chromosome length (Mbp)") +
  theme(panel.background = element_blank(),
        axis.line = element_line(size = 1),
        axis.text = element_text(size = 10),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.text = element_text(size = 10))

#chr length and rate 
ggplot(rec_rate_mean, aes(rec_rate_mean$chr_length,rec_rate_mean$`2mb`)) +
  geom_point() +
  geom_smooth(method = "lm") +
  labs(title = "Correlation rec rate (2Mb windows) and chromosome size") +
  ylab("cM/Mb") +
  xlab("Chromosome length (Mbp)") +
  theme(panel.background = element_blank(),
        axis.line = element_line(size = 1),
        axis.text = element_text(size = 10),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.text = element_text(size = 10))

ggplot(rec_rate_mean, aes(rec_rate_mean$chr_length,rec_rate_mean$marker_density)) +
  geom_point() +
  geom_smooth(method = "lm") +
  labs(title = "Correlation marker density and chromosome size") +
  ylab("Marker density") +
  xlab("Chromosome length (Mbp)") +
  theme(panel.background = element_blank(),
        axis.line = element_line(size = 1),
        axis.text = element_text(size = 10),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.text = element_text(size = 10))

ggplot(rec_rate_mean, aes(rec_rate_mean$marker_density, rec_rate_mean$rate)) +
  geom_point() +
  geom_smooth(method = "lm") +
  labs(title = "Correlation marker density and recombination rate") +
  ylab("cM/Mb") +
  xlab("Marker density (per Mb)") +
  theme(panel.background = element_blank(),
        axis.line = element_line(size = 1),
        axis.text = element_text(size = 10),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.text = element_text(size = 10))

ggplot(rec_rate_mean, aes(rec_rate_mean$marker_density, rec_rate_mean$`2mb`)) +
  geom_point() +
  geom_smooth(method = "lm") +
  labs(title = "Correlation marker density and recombination rate (window)") +
  ylab("cM/Mb") +
  xlab("Marker density (per Mb)") +
  theme(panel.background = element_blank(),
        axis.line = element_line(size = 1),
        axis.text = element_text(size = 10),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.text = element_text(size = 10))

#coorelation differetn measures of rec rate
ggplot(rec_rate_mean, aes(rec_rate_mean$rate, rec_rate_mean$`2mb`)) +
  geom_point() +
  #geom_smooth(method = "lm") +
  labs(title = "Correlation recombination rate maplength/chrsize and window based") +
  ylab("cM/Mb (windows)") +
  xlab("cM/Mb (maplength/chrsize)") +
  theme(panel.background = element_blank(),
        axis.line = element_line(size = 1),
        axis.text = element_text(size = 10),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.text = element_text(size = 10))

#correlation different measures of rec rate
ggplot(rec_rate_mean, aes(rec_rate_mean$rate, rec_rate_mean$loess)) +
  geom_point() +
  #geom_smooth(method = "lm") +
  labs(title = "Correlation recombination rate maplength/chrsize and loess") +
  ylab("cM/Mb (loess)") +
  xlab("cM/Mb (maplength/chrsize)") +
  theme(panel.background = element_blank(),
        axis.line = element_line(size = 1),
        axis.text = element_text(size = 10),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.text = element_text(size = 10))

#coorelation differetn measures of rec rate
ggplot(rec_rate_mean, aes(rec_rate_mean$loess, rec_rate_mean$`2mb`)) +
  geom_point() +
  #geom_smooth(method = "lm") +
  labs(title = "Correlation recombination rate loess and window based") +
  ylab("cM/Mb (window based)") +
  xlab("cM/Mb (loess)") +
  theme(panel.background = element_blank(),
        axis.line = element_line(size = 1),
        axis.text = element_text(size = 10),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.text = element_text(size = 10))

#chr length and rate orange
ggplot(rec_rate_mean, aes(rec_rate_mean$chr_length,rec_rate_mean$`2mb`)) +
  #geom_point() +
  geom_smooth(method = "lm", colour="#FAAB36", fill="#FAAB36") +
  geom_pointrange(aes(ymin=rec_rate_mean$`2mb`-rec_rate_mean$`2mb_sd`, ymax=rec_rate_mean$`2mb`+rec_rate_mean$`2mb_sd`), size=0.1) +
  labs(title = "Correlation rec rate (2Mb windows) and chromosome size") +
  ylab("cM/Mb") +
  xlab("Chromosome length (Mbp)") +
  theme(panel.background = element_blank(),
        axis.line = element_line(size = 0.2, colour = "grey"),
        axis.text = element_text(size = 10),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.text = element_text(size = 10))

#chr length and rate blue
ggplot(rec_rate_mean, aes(rec_rate_mean$chr_length,rec_rate_mean$`2mb`)) +
  #geom_point() +
  geom_smooth(method = "lm", colour="#249EA0", fill="#249EA0") +
  geom_pointrange(aes(ymin=rec_rate_mean$`2mb`-rec_rate_mean$`2mb_sd`, ymax=rec_rate_mean$`2mb`+rec_rate_mean$`2mb_sd`), size=0.1) +
  labs(title = "Correlation rec rate (2Mb windows) and chromosome size") +
  ylab("cM/Mb") +
  xlab("Chromosome length (Mbp)") +
  theme(panel.background = element_blank(),
        axis.line = element_line(size = 0.2, colour = "grey"),
        axis.text = element_text(size = 10),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.text = element_text(size = 10))

#chr length and rate blue, points only contour
ggplot(rec_rate_mean, aes(rec_rate_mean$chr_length,rec_rate_mean$`2mb`)) +
  geom_smooth(method = "lm", colour="#249EA0", fill="#249EA0") +
  geom_pointrange(aes(ymin=rec_rate_mean$`2mb`-rec_rate_mean$`2mb_sd`, ymax=rec_rate_mean$`2mb`+rec_rate_mean$`2mb_sd`), size=0.2, shape=21) +
  geom_point(size=3,shape=21, fill="white") +
  labs(title = "Correlation rec rate (2Mb windows) and chromosome size") +
  ylab("cM/Mb") +
  xlab("Chromosome length (Mbp)") +
  theme(panel.background = element_blank(),
        axis.line = element_line(size = 0.2, colour = "grey"),
        axis.text = element_text(size = 12),
        axis.title.x = element_text(size = 12),
        axis.title.y = element_text(size = 12),
        legend.text = element_text(size = 12))

#saved as pdf


#regional variation
#plot recombination rate per Mb (2 Mb window)
ggplot(rec_rate, aes(rec_rate$rel_pos, rec_rate$slidingwindow.3)) +
  geom_point(colour="dark grey") +
  #geom_line() +
  geom_smooth(method = "loess", colour="#249EA0", fill="#249EA0") +
  ylab("cM/Mb") +
  xlab("Relative position") +
  scale_y_continuous(expand = c(0.01,0.05)) +
  scale_x_continuous(expand = c(0.01,0.05)) +
  theme(panel.background = element_blank(),
        axis.line = element_line(size = 1, colour = "grey"),
        axis.text = element_text(size = 20),
        axis.title.x = element_text(size = 20),
        axis.title.y = element_text(size = 20),
        legend.text = element_text(size = 20))
## Warning: Removed 5 rows containing non-finite values (stat_smooth).
## Warning: Removed 5 rows containing missing values (geom_point).

ggplot(rec_rate, aes((sqrt((rec_rate$rel_pos-50)^2)), rec_rate$slidingwindow.3)) +
  geom_point(colour="dark grey") +
  #geom_line() +
  geom_smooth(method = "loess", colour="#249EA0", fill="#249EA0") +
  ylab("cM/Mb") +
  xlab("Relative position") +
  scale_y_continuous(expand = c(0.01,0.05)) +
  scale_x_continuous(expand = c(0.01,0.05)) +
  theme(panel.background = element_blank(),
        axis.line = element_line(size = 1, colour = "grey"),
        axis.text = element_text(size = 20),
        axis.title.x = element_text(size = 20),
        axis.title.y = element_text(size = 20),
        legend.text = element_text(size = 20))
## Warning: Removed 5 rows containing non-finite values (stat_smooth).

## Warning: Removed 5 rows containing missing values (geom_point).

#create black and white palett
palette <- rep(c("light grey", "white"), length.out = length(unique(rec_rate$map)))

palette_blue <- rep(c("#a7d8d9", "#d3eded"), length.out = length(unique(rec_rate$map)))


#plot recombination rate per Mb (2 Mb window)
ggplot(rec_rate, aes(rec_rate$genome_pos, rec_rate$slidingwindow.3)) +
  geom_rect(aes(xmin=rec_rate$chr_start, xmax=rec_rate$chr_start+rec_rate$chr_length, ymin=0, ymax=16, fill=rec_rate$map, alpha=0.5)) +
  geom_point(size=0.4) +
  geom_line(size=0.2) +
  scale_fill_manual(values = palette_blue) +
  guides(fill=FALSE, alpha=FALSE) +
  ylab("cM/Mb") +
  xlab("Mb") +
  theme(panel.background = element_blank(),
        axis.line = element_line(size = 0.2, colour = "grey"),
        axis.text = element_text(size = 10),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.text = element_text(size = 10))
## Warning: Removed 5 rows containing missing values (geom_point).

ggplot(rec_rate, aes(rec_rate$genome_pos, rec_rate$slidingwindow.3)) +
  geom_rect(aes(xmin=rec_rate$chr_start, xmax=rec_rate$chr_start+rec_rate$chr_length, ymin=0, ymax=16, fill=rec_rate$map, alpha=0.5)) +
  geom_point(size=0.4) +
  #geom_line(size=0.2) +
  geom_smooth(method = "loess", span=0.1,colour="#249EA0", fill="#249EA0") +
  scale_fill_manual(values = palette_blue) +
  guides(fill=FALSE, alpha=FALSE) +
  ylab("cM/Mb") +
  xlab("Mb") +
  theme(panel.background = element_blank(),
        axis.line = element_line(size = 0.2, colour = "grey"),
        axis.text = element_text(size = 10),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.text = element_text(size = 10))
## Warning: Removed 5 rows containing non-finite values (stat_smooth).

## Warning: Removed 5 rows containing missing values (geom_point).

ggplot(rec_rate, aes(rec_rate$genome_pos, rec_rate$slidingwindow.3)) +
  annotate(geom = "rect", xmin=rec_rate$chr_start, xmax=rec_rate$chr_start+rec_rate$chr_length, ymin=-0.2, ymax=16, fill="light grey", colour="white") +
  geom_point(size=0.4) +
  geom_smooth(method = "loess", span=0.1,colour="#249EA0", fill="#249EA0") +
  #geom_line(size=0.2) +
  #scale_fill_manual() +
  #guides(fill=FALSE, alpha=FALSE) +
  ylab("cM/Mb") +
  xlab("Mb") +
  scale_y_continuous(expand = c(0.01,0.05)) +
  scale_x_continuous(expand = c(0.01,0.05)) +
  theme(panel.background = element_blank(),
        axis.line = element_line(size = 0.5, colour = "grey"),
        axis.text = element_text(size = 12),
        axis.title.x = element_text(size = 12),
        axis.title.y = element_text(size = 12),
        legend.text = element_text(size = 12))
## Warning: Removed 5 rows containing non-finite values (stat_smooth).

## Warning: Removed 5 rows containing missing values (geom_point).